scholarly journals Mobile phone data analysis guidelines: applications to monitoring physical distancing and modeling COVID-19

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Mathew Vinhhoa Kiang ◽  
Kenth Engø-Monsen ◽  
Navin Vembar ◽  
Satchit Balsari ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Data Streams ◽  
Mobile Phone Data ◽  
Central Feature ◽  
Technical Aspects ◽  
Aggregated Data ◽  
Mobility Data ◽  
Area Standard ◽  
Research And Policy

There has been a recent surge of interest in using mobility data from mobile phones to monitor social distancing and model the spread of SARS-COV-2, the virus that causes COVID-19. Despite several years of research in this area, standard frameworks for aggregating and making use of different data streams from mobile phones are lacking and difficult to generalize across data providers. Here, we provide a comprehensive set of guidelines for aggregation principles and procedures for different mobile phone data streams, and describe how aggregated data are used in research and policy. We argue that the principles of privacy and data protection are critical in assessing more technical aspects of aggregation, and should be an important central feature guiding partnerships with governments who make use of research products.

scholarly journals Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study

10.2196/10194 ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. e10194 ◽  
Author(s):  
Niclas Palmius ◽  
Kate E A Saunders ◽  
Oliver Carr ◽  
John R Geddes ◽  
Guy M Goodwin ◽  
...  
Keyword(s):  
Mental Health ◽  
Observational Study ◽  
Mobile Phone ◽  
Data Streams ◽  
Regression Models ◽  
Mobile Phone Data

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals Implementation of Mobile Phone Data Collection in the Conduct EPI Comprehensive Review in East and Southern African Countries

2021 ◽  
Vol Special Issue (2) ◽  
pp. 55-62
Author(s):  
Isah Mohammed Bello ◽  
Abubakar Sadiq Umar ◽  
Godwin Ubong Akpan ◽  
Joseph Okeibunor ◽  
Chukwudi Shibeshi ◽  
...  
Keyword(s):  
Data Collection ◽  
Mobile Phone ◽  
Real Time ◽  
Mobile Phones ◽  
Health Sector ◽  
Mobile Phone Data ◽  
Data Detection ◽  
Time Data ◽  
African Countries ◽  
Comprehensive Review

Mobile phone data collection tools are increasingly becoming very usable collecting, collating and analysing data in the health sector. In this paper, we documented the experiences with mobile phone data collection, collation and analysis in 5 countries of the East and Southern African, using Open Data Kit (ODK), where questionnaires were designed and coded on an XML form, uploaded and data collected using Android-Based mobile phones, with a web-based system to monitor data in real-time during EPI comprehensive review. The ODK interface supports in real-time monitoring of the flow of data, detection of missing or incomplete data, coordinate location of all locations visited, embedded charts for basic analysis. It also minimized data quality errors at entry level with the use of validation codes and constraint developed into the checklist. These benefits, combined with the improvement that mobile phones offer over paper-based in terms of timeliness, data loss, collation, and real-time data collection, analysis and uploading difficulties, make mobile phone data collection a feasible method of data collection that needs to be further explored in the conduct of all surveys in the organization.

scholarly journals Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case

2021 ◽  
Vol 13 (24) ◽  
pp. 13713
Author(s):  
Xuesong Gao ◽  
Hui Wang ◽  
Lun Liu
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Future Research ◽  
Aggregate Level ◽  
Individual Level ◽  
Privacy Leakage ◽  
Mobility Data ◽  
Level Data ◽  
Data Source ◽  
Movement Trace

People’s movement trace harvested from mobile phone signals has become an important new data source for studying human behavior and related socioeconomic topics in social science. With growing concern about privacy leakage of big data, mobile phone data holders now tend to provide aggregate-level mobility data instead of individual-level data. However, most algorithms for measuring mobility are based on individual-level data—how the existing mobility algorithms can be properly transformed to apply on aggregate-level data remains undiscussed. This paper explores the transformation of individual data-based mobility metrics to fit with grid-aggregate data. Fifteen candidate metrics measuring five indicators of mobility are proposed and the most suitable one for each indicator is selected. Future research about aggregate-level mobility data may refer to our analysis to assist in the selection of suitable mobility metrics.

scholarly journals Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh

2021 ◽  
Author(s):  
Lauren A. Cowley ◽  
Mokibul Hassan Afrad ◽  
Sadia Isfat Ara Rahman ◽  
Md Mahfuz Al Mamun ◽  
Taylor Chin ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Rural Areas ◽  
National Level ◽  
Control Measures ◽  
Mobile Phone Data ◽  
Population Mobility ◽  
Spatial Spread ◽  
Mobility Data ◽  
Mass Migration ◽  
First Case

AbstractGenomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook ‘Data for Good’ and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

scholarly journals Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data

2021 ◽  
Vol 2 ◽  
Author(s):  
Suxia Gong ◽  
Ismaïl Saadi ◽  
Jacques Teller ◽  
Mario Cools
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data ◽  
Simulation Framework ◽  
Modeling Framework ◽  
Practical Applications ◽  
Mobility Data ◽  
Demand Models ◽  
The Difference ◽  
Using Data

An essential step in agent-based travel demand models is the characterization of the population, including transport-related attributes. This study looks deep into various mobility data in the province of Liège, Belgium. Based on the data stemming from the 2010 Belgian HTS, that is, BELDAM, a Markov chain Monte Carlo (MCMC) sampling method combined with a cross-validation process is used to generate sociodemographic attributes and trip-based variables. Besides, representative micro-samples are calibrated using data about the population structure. As a critical part of travel demand modeling for practical applications in the real-world context, validation using various data sources can contribute to the modeling framework in different ways. The innovation in this study lies in the comparison of outputs of MCMC with mobile phone data. The difference between modeled and observed trip length distributions is studied to validate the simulation framework. The proposed framework infers trips with multiple attributes while preserving the traveler’s sociodemographics. We show that the framework effectively captures the behavioral complexity of travel choices. Moreover, we demonstrate mobile phone data’s potential to contribute to the reliability of travel demand models.

scholarly journals Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sofonias Tessema ◽  
Amy Wesolowski ◽  
Anna Chen ◽  
Maxwell Murphy ◽  
Jordan Wilheim ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Southern Africa ◽  
Human Mobility ◽  
Genetic Data ◽  
Mobile Phone Data ◽  
Malaria Surveillance ◽  
Travel History ◽  
Cross Border ◽  
Mobility Data ◽  
Regional Coordination

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

scholarly journals Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Hsiao-Han Chang ◽  
Amy Wesolowski ◽  
Ipsita Sinha ◽  
Christopher G Jacob ◽  
Ayesha Mahmud ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Genetic Data ◽  
Epidemiological Surveillance ◽  
Mobile Phone Data ◽  
Spatial Spread ◽  
Control Programs ◽  
Mobility Data ◽  
Imported Infections ◽  
National Control

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

scholarly journals Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

2019 ◽  
Vol 26 (3) ◽  
Author(s):  
Shengjie Lai ◽  
Andrea Farnham ◽  
Nick W Ruktanonchai ◽  
Andrew J Tatem
Keyword(s):  
Health Risk ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Spatial Scales ◽  
Human Mobility ◽  
Travel Medicine ◽  
Mobile Phone Data ◽  
Individual Risk ◽  
Spatial And Temporal Scales ◽  
Human Movements

Abstract Rationale for review The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Key findings Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Conclusions Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

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